Course description

This intensive online course, Statistics & Probability: An Introduction for Academic Researchers, is designed specifically for professionals working in medicine, public health, clinical research and allied healthcare fields who need practical, interpretable statistical knowledge for research, audits and decision-making.

Starting from core probability concepts and building to essential applied methods, the course emphasizes intuition, interpretation and correct reporting of results rather than abstract math. Through short lectures, worked examples using real (de-identified) clinical datasets, guided coding notebooks (R and Python), and short assignments, you’ll learn how to design analyses, choose appropriate tests, calculate power and sample size, evaluate diagnostic accuracy, build regression models (linear, logistic, Cox), and present results clearly for journals, ethics boards and stakeholders.

Key features:

• Domain-focused examples (clinical trials, observational cohorts, diagnostic studies).

• Hands-on labs in R and Python plus Excel templates for quick checks.

• Emphasis on reproducible workflows, transparent reporting and avoiding common misinterpretations (p-hacking, misuse of p-values, confounding).

• Practical guidance for planning studies, calculating sample sizes and writing statistical sections for protocols and manuscripts.

Who should take this course: clinicians, clinical research staff, epidemiologists, public-health practitioners, medical students, nurses, data managers, and anyone in healthcare who interprets or produces statistical analyses.

Format & time: modular online format with estimated 6–10 hours/week for a 6–8 week schedule (self-paced options available). Certificate of completion provided after passing assessments.

What will i learn?

• Explain fundamental probability concepts and common probability distributions (Bernoulli, Binomial, Poisson, Normal).
• Calculate and interpret descriptive statistics and graphical summaries for clinical datasets.
• Formulate research hypotheses and choose appropriate significance tests (t-test, chi-square, nonparametric alternatives).
• Compute and interpret confidence intervals, p-values, and effect sizes; explain their limitations.
• Design and perform basic sample-size and power calculations for common study types.
• Fit, check and interpret linear and logistic regression models including adjustment for confounders and interaction terms.
• Perform and interpret survival analysis basics (Kaplan–Meier curves, log-rank test, Cox proportional hazards model).
• Evaluate diagnostic test accuracy using sensitivity, specificity, predictive values and ROC curves / AUC.
• Apply basic Bayesian reasoning for clinical decision-making (conceptual introduction).
• Build reproducible analysis workflows (scripted analyses, versioning, literate programming) and prepare transparent statistical reporting for manuscripts or protocols.
• Recognize common pitfalls (multiple comparisons, selective reporting, collider bias) and how to mitigate them.
• Translate statistical results into clear clinical conclusions and communicate uncertainty effectively to non-statistical audiences.

Frequently asked question